Geometric representations.
Representations to describe 3D objects can be categorized into two groups: discrete and continuous.
Common discrete representations are voxel grids [1]}, [2]}, [3]}, octrees [4]}, [5]}, volumetric signed distance functions (SDFs) [6]}, [7]}, [8]}, point-clouds [9]}, [10]}, [11]}, and meshes [12]}, [13]}, [14]}, [15]}, [16]}.
Even though being widely used, these representations pose important challenges.
Voxel grids and volumetric-SDFs can be easily processed with deep learning frameworks, but are memory inefficient [17]}, [18]}, [19]}. Point-clouds are more memory efficient to process [20]}, [21]}, but do not contain any surface information and thus fail to capture fine details. Meshes are more expressive, but their topology and discretization introduce additional challenges.
